Study on the Mechanism of Ginseng in the Treatment of Lung Adenocarcinoma Based on Network Pharmacology

Background. Ginseng, a traditional Chinese medicine, was used to prevent and treat many diseases such as diabetes, inflammation, and cancer. In recent years, there are some reports about the treatment of lung adenocarcinoma with ginseng monomer compounds, but there is no systematic study on the related core targets and mechanism of ginseng in the treatment of lung adenocarcinoma up to now. Therefore, this study systematically and comprehensively studied the molecular mechanism of ginseng in the treatment of lung adenocarcinoma based on network pharmacology and further proved the potential targets by A549 cell experiments for the first time. Methods. The targets of disease and drug were obtained from Gene database. Subsequently, the compound-target network was constructed, and the core potential targets were screened out by plug-in into Cytoscape. Furthermore, the core targets and mechanism of ginseng in the treatment of lung adenocarcinoma were verified by MTT test, cell scratch test, immunohistochemistry, and qRT-PCR. Results. 1791 disease targets and 144 drug targets were obtained by searching the Gene database. Meanwhile, 15 core targets were screened out: JUN, MAPK8, PTGS2, CASP3, VEGFA, MMP9, AKT1, TNF, FN1, FOS, MMP782, IL-1β, IL-2, ICAM1, and HMOX1. The results of cell experiments indicate that ginseng could treat lung adenocarcinoma by cell proliferation, migration, and apoptosis. In addition, according to the results of the 15 core targets by qRT-PCR, JUN, IL-1β, IL-2, ICAM1, HMOX1, MMP9, and MMP2 are upregulated core targets, while PTGS2 and TNF are downregulated core targets. Conclusion. This study systematically and comprehensively studied 15 core targets by network pharmacology for the first time. Subsequently, it is verified that 9 core targets for ginseng treatment of lung adenocarcinoma, namely, JUN, IL-1β, IL-2, ICAM1, HMOX1, MMP9, MMP2, PTGS2, and TNF, are closely related to the proliferation, migration, and apoptosis of lung adenocarcinoma cells. This study has reference value for the clinical application of ginseng in the treatment of lung adenocarcinoma.

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Mechanism of Action of Bu-Fei-Yi-Shen Formula in Treating Chronic Obstructive Pulmonary Disease Based on Network Pharmacology Analysis and Molecular Docking Validation

BioMed Research International ◽

10.1155/2020/9105972 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Longchuan Wu ◽

Yu Chen ◽

Jiao Yi ◽

Yi Zhuang ◽

Lei Cui ◽

...

Keyword(s):

Molecular Docking ◽

Mechanism Of Action ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Chronic Obstructive ◽

Biological Functions ◽

Active Ingredients ◽

Obstructive Pulmonary Disease ◽

The Core ◽

Target Network

Objective. To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation. Methods. First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identified through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways. Results. In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the effect of BFYSF on treating COPD. Conclusion. BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.

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Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6077698 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Jing Xie ◽

Jun Wu ◽

Sihui Yang ◽

Huaijun Zhou

Keyword(s):

Molecular Docking ◽

Drug Targets ◽

Aloe Vera ◽

Beta Carotene ◽

Network Pharmacology ◽

Potential Mechanism ◽

Active Ingredients ◽

Protein Protein Interaction ◽

Target Network ◽

Molecule Docking

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

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Mechanism of Fei-Xian Formula in the Treatment of Pulmonary Fibrosis on the Basis of Network Pharmacology Analysis Combined with Molecular Docking Validation

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6658395 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Xiao-Li Chen ◽

Cheng Tang ◽

Qing-Ling Xiao ◽

Zhong-Hua Pang ◽

Dan-Dan Zhou ◽

...

Keyword(s):

Molecular Docking ◽

Virus Infection ◽

Pulmonary Fibrosis ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Core Network ◽

Active Ingredients ◽

Molecular Docking Study ◽

The Core ◽

Target Network

Objective. This study aimed to clarify the mechanism of Fei-Xian formula (FXF) in the treatment of pulmonary fibrosis based on network pharmacology analysis combined with molecular docking validation. Methods. Firstly, ingredients in FXF with pharmacological activities, together with specific targets, were identified based on the BATMA-TCM and TCMSP databases. Then, targets associated with pulmonary fibrosis, which included pathogenic targets as well as those known therapeutic targets, were screened against the CTD, TTD, GeneCards, and DisGeNet databases. Later, Cytoscape was employed to construct a candidate component-target network of FXF for treating pulmonary fibrosis. In addition, for nodes within the as-constructed network, topological parameters were calculated using CytoHubba plug-in, and the degree value (twice as high as the median degree value for all the nodes) was adopted to select core components as well as core targets of FXF for treating pulmonary fibrosis, which were subsequently utilized for constructing the core network. Furthermore, molecular docking study was carried out on those core active ingredients together with the core targets using AutoDock Vina for verifying results of network pharmacology analysis. At last, OmicShare was employed for enrichment analysis of the core targets. Results. Altogether 12 active ingredients along with 13 core targets were identified from our constructed core component-target network of FXF for the treatment of pulmonary fibrosis. As revealed by enrichment analysis, the 13 core targets mostly concentrated in regulating biological functions, like response to external stimulus (from oxidative stress, radiation, UV, chemical substances, and virus infection), apoptosis, cell cycle, aging, immune process, and protein metabolism. In addition, several pathways, like IL-17, AGE-RAGE, TNF, HIF-1, PI3K-AKT, NOD-like receptor, T/B cell receptor, and virus infection-related pathways, exerted vital parts in FXF in the treatment of pulmonary fibrosis. Conclusions. FXF can treat pulmonary fibrosis through a “multicomponent, multitarget, and multipathway” mean. Findings in this work lay foundation for further exploration of the FXF mechanism in the treatment of pulmonary fibrosis.

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Mapping drug-target interactions and synergy in multi-molecular therapeutics for pressure-overload cardiac hypertrophy

npj Systems Biology and Applications ◽

10.1038/s41540-021-00171-z ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Aparna Rai ◽

Vikas Kumar ◽

Gaurav Jerath ◽

C. C. Kartha ◽

Vibin Ramakrishnan

Keyword(s):

Cardiac Hypertrophy ◽

Protein Interactions ◽

Drug Targets ◽

Pressure Overload ◽

Network Pharmacology ◽

In Vivo Assays ◽

Drug Molecules ◽

Molecular Therapeutics ◽

Target Network

AbstractAdvancements in systems biology have resulted in the development of network pharmacology, leading to a paradigm shift from “one-target, one-drug” to “target-network, multi-component therapeutics”. We employ a chimeric approach involving in-vivo assays, gene expression analysis, cheminformatics, and network biology to deduce the regulatory actions of a multi-constituent Ayurvedic concoction, Amalaki Rasayana (AR) in animal models for its effect in pressure-overload cardiac hypertrophy. The proteomics analysis of in-vivo assays for Aorta Constricted and Biologically Aged rat models identify proteins expressed under each condition. Network analysis mapping protein–protein interactions and synergistic actions of AR using multi-component networks reveal drug targets such as ACADM, COX4I1, COX6B1, HBB, MYH14, and SLC25A4, as potential pharmacological co-targets for cardiac hypertrophy. Further, five out of eighteen AR constituents potentially target these proteins. We propose a distinct prospective strategy for the discovery of network pharmacological therapies and repositioning of existing drug molecules for treating pressure-overload cardiac hypertrophy.

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Research on active compounds regarding SGMD for the treatment of COVID-19 based on network pharmacology and molecular docking

10.21203/rs.3.rs-60439/v1 ◽

2020 ◽

Author(s):

Ma Donglai ◽

Yuxin Jia ◽

Mingdong Si ◽

Huigai Sun ◽

Huiru Du ◽

...

Keyword(s):

Molecular Docking ◽

Drug Targets ◽

Function Analysis ◽

Chemical Constituents ◽

Binding Energies ◽

Network Pharmacology ◽

Toxic Substances ◽

The Core ◽

Effective Components ◽

Core Components

Abstract Background: Retrieve Curative effect of Six Gentlemen Modified Decoction (SGMD) in treating with coronavirus disease ( COVID-19 ) by network pharmacology and verify its authenticity by molecular docking. Methods: The chemical constituents, effective components, and action targets were screened using TCMSP. COVID-19 related targets were retrieved by the GeneCards and NCBI databases, and drug targets and disease targets were mapped by Venny to obtain potential targets for treatment. The regulatory network of traditional Chinese medicine (TCM) compounds was established with Cytoscape to obtain the key components, and the PPI network and its network topology were established with the Bisogenet and CytoNCA plug-ins to obtain the core targets. Bioconductor was used for GO function analysis and KEGG pathway analysis to obtain the relevant functions and pathways. Results: 173 effective components, 253 targets, and 348 targets related to COVID-19 were obtained after screening, 50 cross targets were shown, and the key components of the top 15 are flavonoids such as quercetin, luteolin, kaempferol, naringenin, licochalcone A, etc. The top 28 core targets include TP53, EGFR, SRC, AR, ABL1, and others. Biological processes such as the responses to metal ions, molecules of bacterial origin, lipopolysaccharide, toxic substances, and oxidative stress were involved. The main pathway involved the AGE−RAGE signaling pathway in diabetic complications as well as the TNF and IL-17 signaling pathways. The average binding energies of the first three core components connected with 6LU7 and 1R42 were -4.16 kJ/mol and -4.12 kJ/mol, respectively.Conclusion: The core compounds of SGMD can spontaneously combine with SARS-CoV-2 3CL hydrolase and ACE2 to treat COVID-19.

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Exploring the Pharmacological Mechanisms of Tripterygium Wilfordii Hook F against Cardiovascular Disease Using Network Pharmacology and Molecular Docking

10.21203/rs.3.rs-151905/v1 ◽

2021 ◽

Author(s):

Bingwu Huang ◽

Chengbin Huang ◽

Liuyan Zhu ◽

Lina Xie ◽

Yi Wang ◽

...

Keyword(s):

Cardiovascular Disease ◽

Drug Targets ◽

R Package ◽

Mitogen Activated Protein Kinase ◽

Network Pharmacology ◽

P Value ◽

Tripterygium Wilfordii ◽

Tripterygium Wilfordii Hook F ◽

The Core ◽

Tripterygium Wilfordii Hook

Abstract Background Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicines (TCM) for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF against CVD remain elusive. This study revealed the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach. Materials and Methods The active compounds were selected by Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were obtained by SwissTargetPrediction database. The CVD-related therapeutic targets were collected by the DrugBank, the GeneCards database and the OMIM database. Protein–protein interaction (PPI) network was generated by STITCH database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug-targets-diseases-pathways was constructed by Cytoscape software. Results The 51 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. Furthermore, AKT1, amyloid precursor protein (APP), Mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) and cellular tumor antigen p53 (TP53) was identified the core targets involved in the mechanism of TwHF on CVD. Top ten GO (biological processes, cellular components and molecular functions) and KEGG pathways were screened with a P value ≤ 0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG. Conclusions These findings demonstrate that the main active compound of TwHF, the core targets and pathways maybe provide new insights into the development of a natural therapy for the prevention and treatment of CVD.

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Exploring Molecular Mechanism of Traditional Chinese Medicine Euphorbiae Semen on Reversing of Multidrug Resistance in Leukemia Based on Network Pharmacology Strategy and Molecular Docking Technology

10.21203/rs.3.rs-55927/v1 ◽

2020 ◽

Author(s):

Xiao Song ◽

Fei Guo ◽

Xiao-Chen Sun ◽

Shu-Yue Wang ◽

Yao-Hui Yuan ◽

...

Keyword(s):

Molecular Docking ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Biological Functions ◽

Active Compounds ◽

The Core ◽

The World ◽

Target Network ◽

Compound Target

Abstract Background: Leukemia was listed by the World Health Organization as one of the five most intractable diseases in the world. The multi-drug resistance (MDR) of leukemia cells limits the efficacy of anti-tumor drugs and is the major reason for the chemotherapy failure and recurrence of leukemia chemotherapy. Some studies have shown that Euphorbiae semen (ES) possesses the characteristics of new therapeutic drugs for MDR. However, the molecular mechanisms and active compounds have not yet been fully clarified. Therefore, there is a need for explore its active compounds and demonstrate its mechanisms through network pharmacology and molecular docking technology.Method: First, the TCMSP database was searched and screened the active compounds of the ES, supplemented with compounds verified by literature, so as to further identify the core compounds in the active ingredient. Simultaneously, the TCMSP and Swiss database were searched to the targets of active compounds, and the targets of reverses leukemia multidrug resistance (RL-MDR) were screened in the relevant databases, such as GeneCards and DrugBank. Then, the targets of active compounds were intersected with RL-MDR targets to obtain potential targets of ES acting on MDR. The compound–target network was constructed by Cytoscape. The target protein–protein interaction network was built using STRING and Cytoscape database. Second, the R language and DAVID database were used to analyse Gene Ontology (GO) biological functions analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways enrichment. Finally, molecular docking method was utilized to investigate the binding activity between the core targets and the active compounds of ES.Results: Compound–target network mainly contained 22 compounds and 81 corresponding targets. Finally, seven components in ES were selected and 10 core targets were identified; Key targets contained JUN, CASP3, MAOA, AR, PPARG, DRD2, ADRA2A, CHRM2, PTGS2 and MAPK14. GO enrichment analysis indicated the main biological functions of potential genes of ES in the treatment of MDR. KEGG pathway enrichment analysis showed the main pathways, mainly including apoptosis, pathways in cancer, p53 signaling pathway, VEGF signaling pathway, TNF signaling pathway and PI3K–Akt signaling pathway. Finally, we chose the top 10 common targets for molecular docking with the 7 active compounds of ES. The results of molecular docking indicated that the compounds of ES, which had good affinity with targets. Conclusion: The molecular mechanism of ES in the treatment of MDR showed the synergistic reaction of multi-compound, multi-target, and multi-pathway of traditional Chinese medicine, which provided ideas for further clinical research.

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Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2022/3747285 ◽

2022 ◽

Vol 2022 ◽

pp. 1-20

Author(s):

Sijie Li ◽

Yong Yang ◽

Wei Zhang ◽

Haiyan Li ◽

Wantong Yu ◽

...

Keyword(s):

Ischemic Stroke ◽

Molecular Docking ◽

Western Blot ◽

Signaling Pathway ◽

Cellular Response ◽

Network Pharmacology ◽

Active Compounds ◽

The Core ◽

Target Network ◽

Compound Target

Purpose. Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment. Methods. All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot. Results. Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis ( p < 0.05 ) and 119 signaling pathways ( p < 0.05 ) in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS. Conclusions. This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.

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Anti-inflammatory Mechanism of Rhein in Treating Asthma Based on Network Pharmacology

10.21203/rs.2.22156/v1 ◽

2020 ◽

Author(s):

Junfang Feng ◽

Ou Chen ◽

Yibiao Wang

Keyword(s):

Network Pharmacology ◽

Ppi Network ◽

Operational Mechanism ◽

Inflammatory Mechanism ◽

Response Network ◽

Target Network ◽

Gene Database ◽

Anti Inflammatory ◽

Ncbi Gene Database ◽

Network Software

Abstract BackgroundNetwork pharmacology was used to study Rhein -target-pathway and to clarify its anti-inflammatory mechanism in the treatment of asthma.and provide a new idea for the treatment of asthmaMethodsThis method, which allows using network pharmacology to figure out the operational mechanism of Rhein-Target-Pathway, defines the effect of anti-inflammatory in treating asthma. The platform of Traditional Chinese Medicine Molecular Mechanism Bioinformatics, a web server for network, is used to get the corresponding target of Rhein and permit molecular docking. Cytoscape3.7.1, a kind of network software, is used to construct Rhein-predicted target network and analyse network topology. Search anti-inflammatory targets in the database of TTD and then construct the PPI network as well as create protein interaction networks that are combined with the Rhein-predicted target network. The anti-inflammatory targets of Rhein should be presented. The asthma genes of human being can be attained from the database of NCBI Gene Database and construct correspondence vivo response network model. Find Anti-inflammatory targets of Rhein against asthma, screen anti-inflammatory targets of Rhein related with Pathogenesis of asthma. Enrichr database is used to analyse signal pathway from anti-inflammatory targets of Rhein KEGG.ResultsAccording to the study, Rhein corresponds to 17 target proteins, four anti-inflammatory targets of Rhein related to asthma(MAPK14, EGFR, ERBB2, TNFRSF1A) are probably the most important targets where asthma is treated by Rhein.ConclusionsThese four anti-inflammatory targets of Rhein related to asthma are probably the key targets in the treatment of asthma by using Rhein. For the purpose of preventing the occurrence as well as development of asthma and delaying the progress of the disease, one or some of the four anti-inflammatory targets of Rhein related to asthma can be controlled.

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The Use of Traditional Chinese Medicine in Relieving EGFR-TKI-Associated Diarrhea Based on Network Pharmacology and Data Mining

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5530898 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Shuaihang Hu ◽

Wenchao Dan ◽

Jinlei Liu ◽

Peng Ha ◽

Tong Zhou ◽

...

Keyword(s):

Data Mining ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Drug Targets ◽

Kinase Inhibitor ◽

Molecular Structures ◽

Network Pharmacology ◽

Lipinski’S Rule ◽

The Core ◽

Egfr Tki

In this study, the role of traditional Chinese medicine (TCM) in relieving epidermal growth factor receptor-tyrosine kinase inhibitor- (EGFR-TKI-) associated diarrhea was discussed by network pharmacology and data mining. Prediction of drug targets by introducing the EGFR-TKI molecular structures into the SwissTargetPrediction platform and diarrhea-related targets in the DrugBank, GeneCards, DisGeNET, and OMIM databases were obtained. Compounds in the drug-disease target intersection were screened by absorption, distribution, metabolism, and excretion parameters and Lipinski’s rule in Traditional Chinese Medicine Systems Pharmacology. TCM-containing compounds were selected, and information on the property, taste, and meridian tropism of these TCMs was summarized and analyzed. A target-compound-TCM network diagram was constructed, and core targets, compounds, and TCMs were selected. The core targets and components were docked by AutoDock Vina (Version 1.1.2) to explore the target combinations of related compounds and evaluate the docking activity of related targets and compounds. Twenty-three potential therapeutic TCM targets for the treatment of EGFR-TKI-related diarrhea were obtained. There were 339 compounds acting on potential therapeutic targets, involving a total of 402 TCMs. The results of molecular docking showed good binding between the core targets and compounds, and the binding between the core targets and compounds was similar to that of the core target and the recommended drug loperamide. TCMs have multitarget characteristics and are present in a variety of compounds used for relieving EGFR-TKI-associated diarrhea. Antitumor activity and the efficacy of alleviating diarrhea are the pharmacological basis of combining TCMs with EGFR-TKI in the treatment of non-small-cell lung cancer. The core targets, compounds, and TCMs can provide data to support experimental and clinical studies on the relief of EGFR-TKI-associated diarrhea in the future.

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